In recent years, four-dimensional CT (4DCT) imaging protocols utilizing a diagnostic CT scanner and surrogate respiratory signal have been developed at many clinical centers. Using 4DCT image volumes at different respiratory phases, respiratory motion fields can be extracted, thereby allowing estimation of motion trajectory of the tumor. The extracted motion fields can be utilized to warp the image volume and treatment plan at a given reference respiratory phase. However, the motion fields and the motion trajectory may experience changes from the time of treatment planning to the time of treatment delivery. Thus, it is important to develop a method to monitor the potential changes to the motion trajectory to either verify the treatment plan before the actual treatment is delivered or obtain necessary information for treatment replanning. This motivated investigators to extend the 4DCT concept and methodology to the 4D cone-beam CT (4DCBCT) case, in which the acquired cone-beam projections are sorted into different respiratory phases using surrogate respiratory signals. However, there are two fundamental challenges in 4DCBCT that hinder the improvement of its image quality, and thus its applications. The first challenge is a significant violation of the Shannon/Nyquist sampling requirement. When the acquired 600 projections are gated into 8-10 respiratory phases, there are only 60-80 cone-beam projections available to reconstruct each respiratory phase. When standard image reconstruction algorithms, such as Filtered BackProjection (FBP), are applied to highly undersampled data sets, streaking artifacts are rampant in the reconstructed image. The second challenge in 4DCBCT is the sampling pattern of the cone-beam projection data for a given respiratory phase is far from optimal. This makes the use of the available 60-80 projections significantly inefficient. As a consequence, the current 4DCBCT image quality is insufficient to be utilized in routine clinical practice to extract accurate motion profiles either for quality assurance or for treatment replanning purposes. The overall objective of this proposal is to develop an innovative method to improve the 4DCBCT image quality and to demonstrate its use in image-guided radiation therapy. The central enabling technology is a newly proposed image reconstruction scheme by the PI's group, namely Prior Image Constrained Compressed Sensing (PICCS). Using this novel method, preliminary results have demonstrated that CT images can be accurately reconstructed using approximately 10-20 projections, which enables gating of the acquired cone- beam projection into 20-30 respiratory phases. As a result, 4DCBCT imaging can be achieved using a 60- second data acquisition, rather than a prolonged 4-5 minute scan time. In this proposal, the central hypothesis is that the PICCS acquisition and image reconstruction method should enable reconstruction of high temporal resolution and streak-free 4DCBCT images for radiation therapy applications. This new method is referred to as PICCS-4DCBCT. The specific aims of the proposal are to: (1) Optimize the implementation of the PICCS image reconstruction algorithm; (2) Develop and evaluate PICCS-4DCBCT data acquisition protocols; (3) Conduct clinical evaluations using PICCS-4DCBCT. Upon the completion of the project, accurate 4DCBCT using the novel PICCS image reconstruction method will have been developed and validated for image-guided radiation therapy. This new technology will enable accurate extraction of tumor motion information for compensation of the respiratory-related intrafraction and/or interfraction motion before and during treatment delivery.